Machine Learning Engineer - Post Training

Machine Learning Engineer - Post Training

A stealth-stage venture backed by Lux Capital (investors in DeepMind and OpenAI) is developing frontier-scale AI systems for high-impact applications in human health and decision-making. The team is applying LLMs and multimodal agents to complex real-world problems where precision, safety, and interpretability matter.

This role focuses on post-training workflows for large models, including RLHF, reward modeling, and evaluation. Ideal candidates have experience aligning agent behavior with nuanced goals in high-stakes environments. The position involves close collaboration with research and product teams to define evaluation criteria, build scalable RL pipelines, and ship production-grade systems.

Ideal Experience :

• Designing and scaling RL environments for LLMs
• Building high-quality evaluation pipelines for frontier models
• Collaborating with domain experts to define evaluation tools and metrics
• Crafting training datasets and reward functions using LLMs and / or human feedback
• Training large models with RLHF, DPO, or instruction tuning
• Translating abstract requirements into concrete evaluation frameworks and agent behaviors

Bonus :